Research within the agricultural community has shown that management of crop production may be optimized by taking into account spatial variations that often exist within a given farming field. For example, by varying the farming inputs applied to a field according to local conditions within the field, a farmer can optimize crop yield as a function of the inputs being applied while preventing or minimizing environmental damage. This management technique has become known as precision, site-specific, prescription or spatially-variable farming.
The management of a field using precision farming techniques requires the gathering and processing of data relating to site-specific characteristics of the field. Generally, site-specific input data is analyzed in real-time or off-line to generate a prescription map including desired application rates of a farming input. A control system reads data from the prescription map and generates a control signal which is applied to a variable-rate controller adapted to apply a farming input to the field at a rate that varies as a function of the location. Variable-rate controllers may be mounted on agricultural vehicles with attached variable-rate applicators, and may be used to control application rates for applying seed, fertilizer, insecticide, herbicide or other inputs. The effect of the inputs may be analyzed by gathering site-specific yield and moisture content data and correlating this data with the farming inputs, thereby allowing a user to optimize the amounts and combinations of farming inputs applied to the field.
The spatially-variable characteristic data may be obtained by manual measuring, remote sensing, or sensing during field operations. Manual measurements typically involve taking a soil probe and analyzing the soil in a laboratory to determine nutrient data or soil condition data such as soil type or soil classification. Taking manual measurements, however, is labor intensive and, due to high sampling costs, provides only a limited number of data samples. Remote sensing may include taking aerial photographs or generating spectral images or maps from airborne or spaceborne multispectral sensors. Spectral data from remote sensing, however, is often difficult to correlate with a precise position in a field or with a specific quantifiable characteristic of the field. Both manual measurements and remote sensing require a user to conduct an airborne or ground-based survey of the field apart from normal field operations.
Spatially-variable characteristic data may also be acquired during normal field operations using appropriate sensors supported by a combine, tractor or other vehicle. A variety of characteristics may be sensed including soil properties (e.g., organic matter, fertility, nutrients, moisture content, compaction and topography or altitude), crop properties (e.g., height, moisture content or yield), and farming inputs applied to the field (e.g., fertilizers, herbicides, insecticides, seeds, cultural practices or tillage techniques used). Other spatially-variable characteristics may be manually sensed as a field is traversed (e.g., insect or weed infestation or landmarks). As these examples show, characteristics which correlate to a specific location include data related to local conditions of the field, farming inputs applied to the field, and crops harvested from the field.
Obtaining characteristic data during normal field operations is advantageous over manual measuring and remote sensing in that less labor is required, more data samples may be taken, samples may be easier to correlate with specific positions and separate field surveys are not required. Some agricultural vehicles equipped with a sensor for detecting a site-specific characteristic "on the go" provide the user with a numerical display showing the instantaneous value of the characteristic. The cab in a combine, for example, may include a display showing instantaneous yield information (e.g., bu/acre) measured by a grain flow sensor. However, such a display does not show how the characteristic varies throughout an area of the field. Thus, it would be beneficial to provide a system for an agricultural vehicle which includes an electronic display showing a map of a field being worked and visible indicia of a sensed characteristic. Such a display may also show the position of the vehicle on the map. This type of display would provide feedback to a farmer while the field is being worked.
Providing such an electronic display, however, has been difficult because of the high accuracy required to determine the position of an agricultural vehicle within a field and the position at which a characteristic is sampled. Typical positioning systems (e.g., LORAN; GPS) have not been able to accurately distinguish between locations of a vehicle within a field. One navigation system for an on-road vehicle, such as an automobile, corrects for position error on GPS signals by assuming that the vehicle must be on a road defined on a stored digital map. For example, if the detected position of the vehicle is 10 feet from road X and 100 feet from road Y, the system corrects the position to place the vehicle on road X. Such a system, however, cannot correct position signals of an agricultural vehicle in a field since the vehicle may roam freely throughout the field.